Internet of Things platforms for Smart Cities are technologically complex and deploying them at large scale involves high costs and risks. Therefore, pilot schemes that allow validating proof of concepts, experimenting with different technologies and services, and fine-tuning them before migrating them to actual scenarios, are especially important in this context. The IoT platform deployed across the engineering schools of the Universidad Politécnica de Madrid in the Moncloa Campus of International Excellence represents a good example of a test bench for experimentation with Smart City services. This paper presents the main features of this platform, putting special emphasis on the technological challenges faced and on the solutions adopted, as well as on the functionality, services and potential that the platform offers.

The extended markup language (XML) has emerged in the Internet world as a standard representation format, which can be useful to describe and transmit management information. However, XML formats alone do not give formal semantics to it. To solve this question, ontology languages based on the resource description framework can be used to improve the expressiveness of management information specifications. This article presents an approach that uses an XML-based ontology language to define network and system management information. For this, the structures of the Web ontology language known as OWL are analyzed and compared to those used in management definitions, also studying the advantages ontology languages can provide in this area.

New computational and technological paradigms that currently guide developments in the information society, i.e., Internet of things, pervasive technology, or Ubicomp, favor the appearance of new intrusion vectors that can directly affect people’s daily lives. This, together with advances in techniques and methods used for developing new cyber-attacks, exponentially increases the number of cyber threats which affect the information society. Because of this, the development and improvement of technology that assists cybersecurity experts to prevent and detect attacks arose as a fundamental pillar in the field of cybersecurity. Specifically, intrusion detection systems are now a fundamental tool in the provision of services through the internet. However, these systems have certain limitations, i.e., false positives, real-time analytics, etc., which require their operation to be supervised. Therefore, it is necessary to offer architectures and systems that favor an efficient analysis of the data handled by these tools. In this sense, this paper presents a new model of data preprocessing based on a novel distributed computing architecture focused on large-scale datasets such as UGR’16. In addition, the paper analyzes the use of machine learning techniques in order to improve the response and efficiency of the proposed preprocessing model. Thus, the solution developed achieves good results in terms of computer performance. Finally, the proposal shows the adequateness of decision tree algorithms for training a machine learning model by using a large dataset when compared with a multilayer perceptron neural network.

Cyberattacks are part of the continuous race, where research in computer science both contributes to discovering new threats and vulnerabilities and also mitigates them. When new vulnerabilities are not reported but sold to attackers, they are called “zero-days,” and are particularly difficult to identify. Modern intrusion detection systems (IDS) that leverage artificial intelligence (AI) and machine learning (ML) are becoming essential in identifying these cyber threats. This study presents the design of an IDS using ML and Explainable AI (XAI) techniques for real-time classification of various detected cyberattacks. By utilizing frameworks such as Apache Kafka and Spark, along with libraries such as Scikit-learn and SHAP, the system identifies and classifies normal or anomalous network traffic in real-time. The XAI offers the IDS the option to explain the rationale behind each classification. The primary aim of this research is to develop a flexible and scalable IDS that can provide clear explanations for its decisions. The second aim is to compare and analyze different ML models to achieve the best results in terms of accuracy, f1, recall, and precision. Random Forest models proposed in this research article obtained the best results in figuring out the key features identified by the XAI model, which includes Ct_state_ttl, Sttl, Dmean, and Dbytes from the UNSW-NB15 dataset. Finally, this research work introduces different machine learning algorithms with superior performance metrics compared to other real-time classification methods.

Estimating the number of people present in a given venue in real-time is extremely useful from a security, management, and resource optimization perspective. This article presents the architecture of a system based on the use of Wi-Fi sensor devices that allows estimating, almost in real-time, the number of people attending an event that is taking place in a venue. The estimate is based on the analysis of the “probe request” messages periodically transmitted by smartphones to determine the existence of Wi-Fi access points in the vicinity. The method considers the MAC address randomization mechanisms introduced in recent years in smartphones, which prevents the estimation of the number of devices by simply counting different MAC addresses. To solve this difficulty, our Wi-Fi sensors analyze other fields present in the header of the IEEE 802.11 frames, the information elements, to extract a unique fingerprint from each smartphone. The designed system was tested in a set of real scenarios, obtaining an estimate of attendance at different public events with an accuracy close to 95%.